
#Imports
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import time
from fredapi import Fred
import pyodbc
from sqlalchemy import create_engine
from mpl_toolkits.mplot3d import Axes3D
from sklearn.linear_model import LinearRegression

##plot styles
plt.style.use('fivethirtyeight')
pd.set_option('max_columns', 500)
color_pal = plt.rcParams['axes.prop_cycle'].by_key()['color']
matplotlib.rcParams['figure.figsize'] = (12, 8)


#Connections
fred_key = '041a29909d1c85d5241bfe1c2eeba2a8'
#export to sql
connection_string = 'DRIVER={SQL Server};SERVER=DESKTOP-SHVFG6K\SQLEXPRESS;DATABASE=inflation_project;Trusted_Connection=yes;'
engine = create_engine(f"mssql+pyodbc:///?odbc_connect={connection_string}")
#import from sql
conn = pyodbc.connect(
    "Driver={SQL Server};"
    "Server=DESKTOP-SHVFG6K\SQLEXPRESS;"
    "Database=inflation_project;"
    "Trusted_Connection=yes;"
)


fred = Fred(api_key = fred_key)


fred.search('Corporate Profits', order_by='popularity')


# create corporate profits datafram
corp_profits = fred.get_series(series_id = 'CP')
corp_profits_df = pd.DataFrame(corp_profits, columns=['corp_profits_billions'])
corp_profits_df = corp_profits_df.reset_index()
corp_profits_df = corp_profits_df.rename(columns={'index': 'Date'})
corp_profits_df


# Export corporate profits data to mssql

# corp_profits_df.to_sql('corp_profits', engine, index=False, if_exists='replace')
# engine.dispose()


plt.title("Corporate Profits After Tax")
plt.xlabel("Date")
plt.ylabel("Billions of Dollars")
corp_profits.plot(figsize=(10, 5))

<AxesSubplot:title={'center':'Corporate Profits After Tax'}, xlabel='Date', ylabel='Billions of Dollars'>


fred.search('Consumer Price Index for All Urban Consumers', order_by = 'popularity')


# create cpi all data
cpi_all = fred.get_series(series_id = 'CPIAUCSL')
cpi_all_df = pd.DataFrame(cpi_all, columns=['CPI_all'])
cpi_all_df = cpi_all_df.reset_index()
cpi_all_df = cpi_all_df.rename(columns={'index': 'Date'})
cpi_all_df


# Export cpi all data to mssql
# cpi_all_df.to_sql('cpi_all', engine, index=False, if_exists='replace')
# engine.dispose()


plt.title("CPI Total")
plt.xlabel("Date")
plt.ylabel("Index")
cpi_all.plot(figsize=(10, 5))

<AxesSubplot:title={'center':'CPI Total'}, xlabel='Date', ylabel='Index'>


# create gas cpi dataframe
cpi_gas = fred.get_series(series_id = 'CUSR0000SETB01')
cpi_gas_df = pd.DataFrame(cpi_gas, columns=['CPI_gas'])
cpi_gas_df = cpi_gas_df.reset_index()
cpi_gas_df = cpi_gas_df.rename(columns={'index': 'Date'})
cpi_gas_df


# Export cpi gas data to mssql
# cpi_gas_df.to_sql('cpi_gas', engine, index=False, if_exists='replace')
# engine.dispose()


plt.title("CPI Gas")
plt.xlabel("Date")
plt.ylabel("Index")
cpi_gas.plot(figsize=(10, 5))

<AxesSubplot:title={'center':'CPI Gas'}, xlabel='Date', ylabel='Index'>


# Create CPI meats, poultry, fish and eggs dataframe
cpi_meats_poultry_fish_eggs = fred.get_series(series_id = 'CUSR0000SETB01')
cpi_meats_poultry_fish_eggs_df = pd.DataFrame(cpi_meats_poultry_fish_eggs, columns=['CPI_mpfe'])
cpi_meats_poultry_fish_eggs_df = cpi_meats_poultry_fish_eggs_df.reset_index()
cpi_meats_poultry_fish_eggs_df = cpi_meats_poultry_fish_eggs_df.rename(columns={'index': 'Date'})
cpi_meats_poultry_fish_eggs_df


# cpi_meats_poultry_fish_eggs_df.to_sql('cpi_meats_poultry_fish_eggs', engine, index=False, if_exists='replace', method='multi')
# engine.dispose()


plt.title("CPI Meats, Poultry, Fish, Eggs")
plt.xlabel("Date")
plt.ylabel("Index")
cpi_meats_poultry_fish_eggs.plot(figsize=(10, 5))

<AxesSubplot:title={'center':'CPI Meats, Poultry, Fish, Eggs'}, xlabel='Date', ylabel='Index'>


# get merged table from mssql
query = "SELECT * FROM inflation_data"
inflation_data_df = pd.read_sql(query, conn)
conn.close()
inflation_data_df.head()


# correlation between metrics
correlation_matrix = inflation_data_df.corr(method= 'pearson')
sns.heatmap(correlation_matrix, annot=True)
plt.title('Correlation Matrix')
plt.show()


corp_profits_filtered = corp_profits_df[(corp_profits_df['Date'] >= '2020') & (corp_profits_df['Date'] <= '2023')]
cpi_all_filtered = cpi_all_df[(cpi_all_df['Date'] >= '2020') & (cpi_all_df['Date'] <= '2023')]

fig = px.line()

fig.add_trace(go.Scatter(x=corp_profits_filtered['Date'], y=corp_profits_filtered['corp_profits_billions'], name='Corporate Profits'))
fig.add_trace(go.Scatter(x=cpi_all_filtered['Date'], y=cpi_all_filtered['CPI_all'], name='Total CPI', yaxis='y2'))

fig.update_layout(yaxis=dict(title='Billions of Dollars'), yaxis2=dict(title='Index', side='right', overlaying='y'), legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01))

fig.update_yaxes(range=[corp_profits_filtered['corp_profits_billions'].min(), corp_profits_filtered['corp_profits_billions'].max()], secondary_y=False)

fig.update_yaxes(range=[cpi_all_filtered['CPI_all'].min(), cpi_all_filtered['CPI_all'].max()], secondary_y=True)
fig.update_layout(title='Corporate Profits vs Total CPI', xaxis_title='Date')

fig.show()


merged_df = corp_profits_filtered.merge(cpi_all_filtered, on='Date')
sns.regplot(y=merged_df['corp_profits_billions'], x=merged_df['CPI_all'], scatter_kws={'color': 'red'}, line_kws={'color': 'blue'})

plt.title('Regression Plot - Total CPI vs Corporate Profits')
plt.ylabel('Billions of Dollars')
plt.xlabel('Index')

plt.show()


fig = make_subplots(rows=2, cols=1, shared_xaxes=True)

cpi_gas_filtered = inflation_data_df[(inflation_data_df['Date'] >= '2020') & (inflation_data_df['Date'] <= '2023')]
gas_price_filtered = inflation_data_df[(inflation_data_df['Date'] >= '2020') & (inflation_data_df['Date'] <= '2023')]
xom_filtered = inflation_data_df[(inflation_data_df['Date'] >= '2020') & (inflation_data_df['Date'] <= '2023')]
xom_filtered = xom_filtered.dropna(subset=['XOM_gross_profits_millions'])

fig = px.line()
fig.add_trace(go.Scatter(x=cpi_gas_filtered['Date'], y=cpi_gas_filtered['CPI_gas'], name='CPI Gas'))
fig.add_trace(go.Scatter(x=gas_price_filtered['Date'], y=gas_price_filtered['avg_price_gas_gallon'], name='Average Price per Gallon', yaxis='y2'))
fig.add_trace(go.Scatter(x=xom_filtered['Date'], y=xom_filtered['XOM_gross_profits_millions'], name='Exxon Mobil Gross Profits', yaxis='y3'))

fig.update_layout(xaxis=dict(domain=[0, 0.92]),
                  yaxis=dict(title='Index'), 
                  yaxis2=dict(title='Price (Dollars)', side='right', overlaying='y', anchor='x'), 
                  yaxis3=dict(title='Millions of Dollars', side='right', overlaying='y', position=1), 
                  legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01))
fig.update_yaxes(range=[cpi_gas_filtered['CPI_gas'].min(), cpi_gas_filtered['CPI_gas'].max()], secondary_y=False)
fig.update_yaxes(range=[gas_price_filtered['avg_price_gas_gallon'].min(), gas_price_filtered['avg_price_gas_gallon'].max()], secondary_y=True)
fig.update_yaxes(range=[xom_filtered['XOM_gross_profits_millions'].min(), xom_filtered['XOM_gross_profits_millions'].max()], secondary_y=True)
fig.update_layout(title='CPI Gas vs Average Price of Gas per Gallon vs Exxon Mobil Gross Profits', xaxis_title='Date')

fig.show()


selected_columns = ['CPI_gas', 'avg_price_gas_gallon', 'XOM_gross_profits_millions']
subset_df = inflation_data_df[selected_columns]
correlation_matrix = subset_df.corr(method= 'pearson')
sns.heatmap(correlation_matrix, annot=True)
plt.title('Correlation Matrix - Gasoline')
plt.show()


sns.regplot(x=inflation_data_df['avg_price_gas_gallon'], y=inflation_data_df['XOM_gross_profits_millions'], scatter_kws={'color': 'red'}, line_kws={'color': 'blue'})

plt.title('Regression Plot - Exxon Mobil Gross Profits vs Average Price of Gas per Gallon')
plt.xlabel('Price (Dollars)')
plt.ylabel('Millions of Dollars')


plt.show()


df = inflation_data_df
df = df.dropna(subset=['XOM_gross_profits_millions'])

# Extract the columns as numpy arrays
CPI_gas = df['CPI_gas'].values
avg_price_gas_gallon = df['avg_price_gas_gallon'].values
XOM_gross_profits_millions = df['XOM_gross_profits_millions'].values

# Prepare the feature matrix
X = np.column_stack((CPI_gas, avg_price_gas_gallon))

# Create an instance of the LinearRegression model
model = LinearRegression()

# Fit the model to the data
model.fit(X, XOM_gross_profits_millions)

# Obtain the coefficients and intercept
coefficients = model.coef_
intercept = model.intercept_

# Create a meshgrid for the features
CPI_gas_plot, avg_price_gas_gallon_plot = np.meshgrid(np.linspace(CPI_gas.min(), CPI_gas.max(), 20),
                                                     np.linspace(avg_price_gas_gallon.min(), avg_price_gas_gallon.max(), 20))

# Predict the target variable for the meshgrid points
X_plot = np.column_stack((CPI_gas_plot.ravel(), avg_price_gas_gallon_plot.ravel()))
XOM_gross_profits_millions_plot = model.predict(X_plot)

# Create a 3D plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(CPI_gas, avg_price_gas_gallon, XOM_gross_profits_millions, color='blue', label='Data')

tri = ax.plot_trisurf(CPI_gas, avg_price_gas_gallon, XOM_gross_profits_millions, cmap='viridis', alpha=0.5)
tri.set_edgecolor('k')

ax.set_xlabel('CPI Gas')
ax.set_ylabel('Average Price (Dollars)')
ax.set_zlabel('Exxon Mobil Gross Profits (Millions)')
plt.show()


fig = make_subplots(rows=2, cols=1, shared_xaxes=True)

cpi_mpfe_filtered = inflation_data_df[(inflation_data_df['Date'] >= '2020') & (inflation_data_df['Date'] <= '2023')]
egg_price_filtered = inflation_data_df[(inflation_data_df['Date'] >= '2020') & (inflation_data_df['Date'] <= '2023')]
calm_filtered = inflation_data_df[(inflation_data_df['Date'] >= '2020') & (inflation_data_df['Date'] <= '2023')]

calm_filtered = calm_filtered.dropna(subset=['CALM_gross_profits_millions'])

fig = px.line()

fig.add_trace(go.Scatter(x=cpi_mpfe_filtered['Date'], y=cpi_mpfe_filtered['CPI_mpfe'], name='CPI Meats, Poulty, Fish, Eggs'))
fig.add_trace(go.Scatter(x=egg_price_filtered['Date'], y=egg_price_filtered['avg_egg_price_dozen'], name='Average Price of Eggs per Dozen', yaxis='y2'))
fig.add_trace(go.Scatter(x=calm_filtered['Date'], y=calm_filtered['CALM_gross_profits_millions'], name='Cal-Maine Foods Gross Profits', yaxis='y3'))

fig.update_layout(xaxis=dict(domain=[0, 0.92]),
                  yaxis=dict(title='Index'), 
                  yaxis2=dict(title='Price (Dollars)', side='right', overlaying='y', anchor='x'), 
                  yaxis3=dict(title='Millions of Dollars', side='right', overlaying='y', position=1), 
                  legend=dict(yanchor="top", y=0.99, xanchor="left", x=0.01))

fig.update_yaxes(range=[cpi_mpfe_filtered['CPI_mpfe'].min(), cpi_mpfe_filtered['CPI_mpfe'].max()], secondary_y=False)
fig.update_yaxes(range=[gas_price_filtered['avg_egg_price_dozen'].min(), gas_price_filtered['avg_egg_price_dozen'].max()], secondary_y=True)
fig.update_yaxes(range=[xom_filtered['CALM_gross_profits_millions'].min(), xom_filtered['XOM_gross_profits_millions'].max()], secondary_y=True)

fig.update_layout(title='CPI MFPE vs Average Price of Eggs per Dozen vs Cal-Maine Foods Gross Profits', xaxis_title='Date', width=800)

fig.show()


selected_columns = ['CPI_mpfe', 'avg_egg_price_dozen', 'CALM_gross_profits_millions']
subset_df = inflation_data_df[selected_columns]
correlation_matrix = subset_df.corr(method= 'pearson')
sns.heatmap(correlation_matrix, annot=True)
plt.title('Correlation Matrix - Eggs')
plt.show()


sns.regplot(x=inflation_data_df['avg_egg_price_dozen'], y=inflation_data_df['CALM_gross_profits_millions'], scatter_kws={'color': 'red'}, line_kws={'color': 'blue'})

plt.title('Regression Plot - Cal-Maine Foods Profits vs Price of Eggs per Dozen')
plt.xlabel('Average Price of Eggs per Dozen')
plt.ylabel('Millions of Dollars')


plt.show()


df = inflation_data_df
df = df.dropna(subset=['CALM_gross_profits_millions'])

# Extract the columns as numpy arrays
CPI_mpfe = df['CPI_mpfe'].values
avg_egg_price_dozen = df['avg_egg_price_dozen'].values
CALM_gross_profits_millions = df['CALM_gross_profits_millions'].values

# Prepare the feature matrix
X = np.column_stack((CPI_mpfe, avg_egg_price_dozen))

# Create an instance of the LinearRegression model
model = LinearRegression()

# Fit the model to the data
model.fit(X, CALM_gross_profits_millions)

# Obtain the coefficients and intercept
coefficients = model.coef_
intercept = model.intercept_

# Create a meshgrid for the features
CPI_mpfe_plot, avg_egg_price_dozen_plot = np.meshgrid(np.linspace(CPI_mpfe.min(), CPI_mpfe.max(), 20),
                                                     np.linspace(avg_egg_price_dozen.min(), avg_egg_price_dozen.max(), 20))

# Predict the target variable for the meshgrid points
X_plot = np.column_stack((CPI_mpfe_plot.ravel(), avg_egg_price_dozen_plot.ravel()))
CALM_gross_profits_millions_plot = model.predict(X_plot)

# Create a 3D plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(CPI_gas, avg_price_gas_gallon, XOM_gross_profits_millions, color='blue', label='Data')

tri = ax.plot_trisurf(CPI_mpfe, avg_egg_price_dozen, CALM_gross_profits_millions, cmap='viridis', alpha=0.5)
tri.set_edgecolor('k')

ax.set_xlabel('CPI MPFE')
ax.set_ylabel('Average Price of Eggs per Dozen')
ax.set_zlabel('Cal-Maine Foods Gross Profits')
plt.show()

	id	realtime_start	realtime_end	title	observation_start	observation_end	frequency	frequency_short	units	units_short	seasonal_adjustment	seasonal_adjustment_short	last_updated	popularity	notes
series id
CP	CP	2023-06-20	2023-06-20	Corporate Profits After Tax (without IVA and C...	1947-01-01	2023-01-01	Quarterly	Q	Billions of Dollars	Bil. of $	Seasonally Adjusted Annual Rate	SAAR	2023-05-25 07:53:13-05:00	71	BEA Account Code: A055RC
PI	PI	2023-06-20	2023-06-20	Personal Income	1959-01-01	2023-04-01	Monthly	M	Billions of Dollars	Bil. of $	Seasonally Adjusted Annual Rate	SAAR	2023-05-26 07:43:16-05:00	62	BEA Account Code: A065RC Personal income is th...
A053RC1Q027SBEA	A053RC1Q027SBEA	2023-06-20	2023-06-20	National income: Corporate profits before tax ...	1947-01-01	2023-01-01	Quarterly	Q	Billions of Dollars	Bil. of $	Seasonally Adjusted Annual Rate	SAAR	2023-05-25 07:53:17-05:00	55	BEA Account Code: A053RC For more information...
BOGZ1FL153064486Q	BOGZ1FL153064486Q	2023-06-20	2023-06-20	Households and Nonprofit Organizations; Direct...	1945-10-01	2023-01-01	Quarterly	Q	Percent	%	Not Seasonally Adjusted	NSA	2023-06-08 15:18:04-05:00	54	Source ID: FL153064486.Q For more information...
QUSPAM770A	QUSPAM770A	2023-06-20	2023-06-20	Total Credit to Private Non-Financial Sector, ...	1947-10-01	2022-10-01	Quarterly, End of Quarter	Q	Percentage of GDP	Percentage of GDP	Not Seasonally Adjusted	NSA	2023-06-12 13:01:02-05:00	50	Credit is provided by domestic banks, all othe...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
N513RC0A144NBEA	N513RC0A144NBEA	2023-06-20	2023-06-20	Corporate profits before tax: Domestic industr...	1998-01-01	2019-01-01	Annual	A	Millions of Dollars	Mil. of $	Not Seasonally Adjusted	NSA	2022-10-12 15:38:06-05:00	1	BEA Account Code: N513RC For more information...
CRDQCABPABIS	CRDQCABPABIS	2023-06-20	2023-06-20	Credit to Private Non-Financial Sector by Dome...	1954-01-01	2022-10-01	Quarterly, End of Quarter	Q	Billions of Canadian Dollars	Bil. of Canadian $	Not Seasonally Adjusted	NSA	2023-06-12 13:03:15-05:00	1	Credit is provided by domestic banks, all othe...
CRDQHKAHABIS	CRDQHKAHABIS	2023-06-20	2023-06-20	Total Credit to Households and Non-Profit Inst...	1990-10-01	2022-10-01	Quarterly, End of Quarter	Q	Billions of Hong Kong Dollars	Bil. of Hong Kong $	Not Seasonally Adjusted	NSA	2023-06-12 13:03:08-05:00	1	Credit is provided by domestic banks, all othe...
N415RC1A027NBEA	N415RC1A027NBEA	2023-06-20	2023-06-20	Corporate profits with inventory valuation adj...	1998-01-01	2022-01-01	Annual	A	Billions of Dollars	Bil. of $	Not Seasonally Adjusted	NSA	2023-03-30 07:56:01-05:00	1	BEA Account Code: N415RC For more information...
QBEGAMUSDA	QBEGAMUSDA	2023-06-20	2023-06-20	Total Credit to General Government, Adjusted f...	1998-10-01	2022-10-01	Quarterly, End of Quarter	Q	Billions of US Dollars	Bil. of US $	Not Seasonally Adjusted	NSA	2023-06-12 13:02:53-05:00	1	Credit is provided by domestic banks, all othe...

	Date	corp_profits_billions
0	1946-01-01	NaN
1	1946-04-01	NaN
2	1946-07-01	NaN
3	1946-10-01	NaN
4	1947-01-01	21.970
...	...	...
304	2022-01-01	2833.431
305	2022-04-01	3043.114
306	2022-07-01	2890.812
307	2022-10-01	2721.294
308	2023-01-01	2663.671

	id	realtime_start	realtime_end	title	observation_start	observation_end	frequency	frequency_short	units	units_short	seasonal_adjustment	seasonal_adjustment_short	last_updated	popularity	notes
series id
CPIAUCSL	CPIAUCSL	2023-06-20	2023-06-20	Consumer Price Index for All Urban Consumers: ...	1947-01-01	2023-05-01	Monthly	M	Index 1982-1984=100	Index 1982-1984=100	Seasonally Adjusted	SA	2023-06-13 07:44:03-05:00	94	The Consumer Price Index for All Urban Consume...
CUSR0000SETA02	CUSR0000SETA02	2023-06-20	2023-06-20	Consumer Price Index for All Urban Consumers: ...	1953-01-01	2023-05-01	Monthly	M	Index 1982-1984=100	Index 1982-1984=100	Seasonally Adjusted	SA	2023-06-13 07:43:02-05:00	82	None
CPILFESL	CPILFESL	2023-06-20	2023-06-20	Consumer Price Index for All Urban Consumers: ...	1957-01-01	2023-05-01	Monthly	M	Index 1982-1984=100	Index 1982-1984=100	Seasonally Adjusted	SA	2023-06-13 07:44:03-05:00	82	The "Consumer Price Index for All Urban Consum...
APU0000708111	APU0000708111	2023-06-20	2023-06-20	Average Price: Eggs, Grade A, Large (Cost per ...	1980-01-01	2023-05-01	Monthly	M	U.S. Dollars	U.S. $	Not Seasonally Adjusted	NSA	2023-06-13 07:37:01-05:00	80	Large white, Grade A chicken eggs, sold in a c...
PCEPI	PCEPI	2023-06-20	2023-06-20	Personal Consumption Expenditures: Chain-type ...	1959-01-01	2023-04-01	Monthly	M	Index 2012=100	Index 2012=100	Seasonally Adjusted	SA	2023-05-26 07:43:07-05:00	78	BEA Account Code: DPCERG The Personal Consump...
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
CUUSA101SAT	CUUSA101SAT	2023-06-20	2023-06-20	Consumer Price Index for All Urban Consumers: ...	1984-01-01	2022-07-01	Semiannual	SA	Index 1982-1984=100	Index 1982-1984=100	Not Seasonally Adjusted	NSA	2023-01-12 07:40:56-06:00	1	None
CUSR0000SERF03	CUSR0000SERF03	2023-06-20	2023-06-20	Consumer Price Index for All Urban Consumers: ...	1989-01-01	2023-05-01	Monthly	M	Index Dec 1986=100	Index Dec 1986=100	Seasonally Adjusted	SA	2023-06-13 07:44:44-05:00	1	None
CUURA102SEHC01	CUURA102SEHC01	2023-06-20	2023-06-20	Consumer Price Index for All Urban Consumers: ...	1982-12-01	2023-05-01	Monthly	M	Index Dec 1982=100	Index Dec 1982=100	Not Seasonally Adjusted	NSA	2023-06-13 07:44:24-05:00	1	None
CUUSA213SAT	CUUSA213SAT	2023-06-20	2023-06-20	Consumer Price Index for All Urban Consumers: ...	1984-01-01	2017-01-01	Annual	A	Index 1982-1984=100	Index 1982-1984=100	Not Seasonally Adjusted	NSA	2018-01-12 09:14:03-06:00	1	This series was discontinued as a result of th...
CUUSA213SEHA	CUUSA213SEHA	2023-06-20	2023-06-20	Consumer Price Index for All Urban Consumers: ...	1984-01-01	2017-01-01	Annual	A	Index 1982-1984=100	Index 1982-1984=100	Not Seasonally Adjusted	NSA	2018-01-12 09:12:25-06:00	1	This series was discontinued as a result of th...

	Date	CPI_all
0	1947-01-01	21.480
1	1947-02-01	21.620
2	1947-03-01	22.000
3	1947-04-01	22.000
4	1947-05-01	21.950
...	...	...
912	2023-01-01	300.536
913	2023-02-01	301.648
914	2023-03-01	301.808
915	2023-04-01	302.918
916	2023-05-01	303.294

	Date	CPI_gas
0	1967-01-01	26.300
1	1967-02-01	26.200
2	1967-03-01	26.100
3	1967-04-01	26.400
4	1967-05-01	26.400
...	...	...
672	2023-01-01	315.662
673	2023-02-01	318.945
674	2023-03-01	304.332
675	2023-04-01	313.479
676	2023-05-01	295.962

Inflation and Corporate Profits¶

Set Up Environment¶

Create FRED Object¶

Search FRED for Corporate Profits Data¶

Corporate Profits After Tax (without IVA and CCAdj)¶

Search FRED for CPI Data¶

Consumer Price Index for All Urban Consumers: All Items in U.S. City Average¶

Consumer Price Index for All Urban Consumers: Gasoline (All Types) in U.S. City Average¶

Consumer Price Index for All Urban Consumers: Meats, Poultry, Fish, and Eggs in U.S. City Average¶

Comparisons¶

Compare Total CPI vs Corporate Profits¶

Compare CPI of Gas, Average Price of Gas per Gallon, and Exxon Mobil Profits¶

Compare CPI of Meats, Poultry, Fish and Eggs, Average Price of Eggs per Dozen, and Cal-Maine Foods Profits¶

	Date	CPI_all	CPI_gas	CPI_mpfe	corp_profits_billions	CALM_gross_profits_millions	XOM_gross_profits_millions	avg_egg_price_dozen	avg_price_gas_gallon
0	1947-01-01	21.48	NaN	NaN	21.970	NaN	NaN	NaN	NaN
1	1947-02-01	21.62	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	1947-03-01	22.00	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	1947-04-01	22.00	NaN	NaN	20.788	NaN	NaN	NaN	NaN
4	1947-05-01	21.95	NaN	NaN	NaN	NaN	NaN	NaN	NaN